Instructions to use juntaoyuan/llawa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use juntaoyuan/llawa with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="juntaoyuan/llawa",
	filename="llama-2-7b-chat-wasm-overfit-q5_k_m.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use juntaoyuan/llawa with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf juntaoyuan/llawa:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf juntaoyuan/llawa:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf juntaoyuan/llawa:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf juntaoyuan/llawa:Q5_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf juntaoyuan/llawa:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf juntaoyuan/llawa:Q5_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf juntaoyuan/llawa:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf juntaoyuan/llawa:Q5_K_M

Use Docker

docker model run hf.co/juntaoyuan/llawa:Q5_K_M

LM Studio
Jan
Ollama
How to use juntaoyuan/llawa with Ollama:
```
ollama run hf.co/juntaoyuan/llawa:Q5_K_M
```

Unsloth Studio

How to use juntaoyuan/llawa with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for juntaoyuan/llawa to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for juntaoyuan/llawa to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for juntaoyuan/llawa to start chatting

Atomic Chat new
Docker Model Runner
How to use juntaoyuan/llawa with Docker Model Runner:
```
docker model run hf.co/juntaoyuan/llawa:Q5_K_M
```

Lemonade

How to use juntaoyuan/llawa with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull juntaoyuan/llawa:Q5_K_M

Run and chat with the model

lemonade run user.llawa-Q5_K_M

List all available models

lemonade list

juntaoyuan commited on Nov 8, 2023

Commit

99cdd57

1 Parent(s): 83e4c38

Update README.md

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README.md +2 -0

README.md CHANGED Viewed

@@ -53,3 +53,5 @@ How do I run AI workloads in serverless functions?
 [ASSISTANT]:
 You can use Wasm to run AI workloads in serverless functions. WasmEdge supports running AI workloads using the WASI-NN interface.
 ```

 [ASSISTANT]:
 You can use Wasm to run AI workloads in serverless functions. WasmEdge supports running AI workloads using the WASI-NN interface.
 ```
+> The `llama-2-7b-chat-wasm-overfit-q5_k_m.gguf` file is the fine-tuned model at epoch 25. It has a training loss of 0.03, and is probably over-fitted. You can try the above questions and see it give poor answers. We believe that training loss at 0.05 to 0.1 is optimal for this model.